docs: def create_from_image(cls, imdata, positions, size, fluxes=None,
                            mask=None, mode='mean', subsampling=1,
                            fix_nan=False):
          """
          Create a discrete point response function (PRF) from image data.
  
          Given a list of positions and size this function estimates an
          image of the PRF by extracting and combining the individual PRFs
          from the given positions.
  
          NaN values are either ignored by passing a mask or can be
          replaced by the mirrored value with respect to the center of the
          PRF.
  
          Note that if fluxes are *not* specified explicitly, it will be
          flux estimated from an aperture of the same size as the PRF
          image. This does *not* account for aperture corrections so often
          will *not* be what you want for anything other than quick-look
          needs.
  
          Parameters
          ----------
          imdata : array
              Data array with the image to extract the PRF from
          positions : List or array or `~astropy.table.Table`
              List of pixel coordinate source positions to use in creating
              the PRF.  If this is a `~astropy.table.Table` it must have
              columns called ``x_0`` and ``y_0``.
          size : odd int
              Size of the quadratic PRF image in pixels.
          mask : bool array, optional
              Boolean array to mask out bad values.
          fluxes : array, optional
              Object fluxes to normalize extracted PRFs. If not given (or
              None), the flux is estimated from an aperture of the same
              size as the PRF image.
          mode : {'mean', 'median'}
              One of the following modes to combine the extracted PRFs:
                  * 'mean':  Take the pixelwise mean of the extracted PRFs.
                  * 'median':  Take the pixelwise median of the extracted PRFs.
          subsampling : int
              Factor of subsampling of the PRF (default = 1).
          fix_nan : bool
              Fix NaN values in the data by replacing it with the
              mirrored value. Assuming that the PRF is symmetrical.
  
          Returns
          -------
          prf : `photutils.psf.sandbox.DiscretePRF`
              Discrete PRF model estimated from data.
          """
  
          # Check input array type and dimension.
          if np.iscomplexobj(imdata):
              raise TypeError('Complex type not supported')
          if imdata.ndim != 2:
              raise ValueError('{0}-d array not supported. '
                               'Only 2-d arrays supported.'.format(imdata.ndim))
          if size % 2 == 0:
              raise TypeError("Size must be odd.")
  
          if fluxes is not None and len(fluxes) != len(positions):
              raise TypeError('Position and flux arrays must be of equal '
                              'length.')
  
          if mask is None:
              mask = np.isnan(imdata)
  
          if isinstance(positions, (list, tuple)):
              positions = np.array(positions)
  
          if isinstance(positions, Table) or \
                  (isinstance(positions, np.ndarray) and
                   positions.dtype.names is not None):
              # One can do clever things like
              # positions['x_0', 'y_0'].as_array().view((positions['x_0'].dtype,
              #                                          2))
              # but that requires positions['x_0'].dtype is
              # positions['y_0'].dtype.
              # Better do something simple to allow type promotion if required.
              pos = np.empty((len(positions), 2))
              pos[:, 0] = positions['x_0']
              pos[:, 1] = positions['y_0']
              positions = pos
  
          if isinstance(fluxes, (list, tuple)):
              fluxes = np.array(fluxes)
  
          if mode == 'mean':
              combine = np.ma.mean
          elif mode == 'median':
              combine = np.ma.median
          else:
              raise Exception('Invalid mode to combine prfs.')
  
          data_internal = np.ma.array(data=imdata, mask=mask)
          prf_model = np.ndarray(shape=(subsampling, subsampling, size, size))
          positions_subpixel_indices = \
              np.array([subpixel_indices(_, subsampling) for _ in positions],
                       dtype=np.int)
  
          for i in range(subsampling):
              for j in range(subsampling):
                  extracted_sub_prfs = []
                  sub_prf_indices = np.all(positions_subpixel_indices == [j, i],
                                           axis=1)
                  if not sub_prf_indices.any():
                      raise ValueError('The source coordinates do not sample '
                                       'all sub-pixel positions. Reduce the '
                                       'value of the subsampling parameter.')
  
                  positions_sub_prfs = positions[sub_prf_indices]
                  for k, position in enumerate(positions_sub_prfs):
                      x, y = position
                      extracted_prf = extract_array(data_internal, (size, size),
                                                    (y, x))
                      # Check shape to exclude incomplete PRFs at the boundaries
                      # of the image
                      if (extracted_prf.shape == (size, size) and
                              np.ma.sum(extracted_prf) != 0):
                          # Replace NaN values by mirrored value, with respect
                          # to the prf's center
                          if fix_nan:
                              prf_nan = extracted_prf.mask
                              if prf_nan.any():
                                  if (prf_nan.sum() > 3 or
                                          prf_nan[size // 2, size // 2]):
                                      continue
                                  else:
                                      extracted_prf = mask_to_mirrored_num(
                                          extracted_prf, prf_nan,
                                          (size // 2, size // 2))
                          # Normalize and add extracted PRF to data cube
                          if fluxes is None:
                              extracted_prf_norm = (np.ma.copy(extracted_prf) /
                                                    np.ma.sum(extracted_prf))
                          else:
                              fluxes_sub_prfs = fluxes[sub_prf_indices]
                              extracted_prf_norm = (np.ma.copy(extracted_prf) /
                                                    fluxes_sub_prfs[k])
                          extracted_sub_prfs.append(extracted_prf_norm)
                      else:
                          continue
                  prf_model[i, j] = np.ma.getdata(
                      combine(np.ma.dstack(extracted_sub_prfs), axis=2))
          return cls(prf_model, subsampling=subsampling)